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Recently, the increasing incidence of malignant melanoma has become a major public health concern owing to its poor prognosis and impact on quality of life. Consuming foods with potent antitumor compounds can help prevent melanoma and maintain skin health. Fucoxanthin (FX), a naturally occurring carotenoid found in brown algae, possesses antitumor properties. However, its bioavailability, safety risks, and in vivo effects and mechanisms against melanoma remain unclear. This research focused on evaluating the safety and prospective antimelanoma impact of simulated gastrointestinal digestion products (FX-ID) on HaCaT and A375 cells.The results indicate that FX-ID exerts negative effects on mitochondria in A375 cells, increases Bax expression, releases Cytochrome C, and activates cleaved caspase-3, ultimately promoting apoptosis. Additionally, FX-ID influences the mitogen-activated protein kinase (MAPK) pathway by enhancing cyclooxygenase-2 (COX-2) and nuclear factor kappa B (NF-κB) levels, consequently facilitating apoptosis and inflammation without significantly impacting HaCaT cells. These findings provide insight into inhibitory mechanism of FX-ID against melanoma, guiding the development of functional foods for prevention.

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Apoptosis , Queratinocitos , Melanoma , Xantófilas , Humanos , Melanoma/metabolismo , Queratinocitos/efectos de los fármacos , Queratinocitos/metabolismo , Apoptosis/efectos de los fármacos , Xantófilas/farmacología , Xantófilas/química , Línea Celular Tumoral , FN-kappa B/metabolismo , FN-kappa B/genética , Digestión , Modelos Biológicos , Ciclooxigenasa 2/metabolismo , Ciclooxigenasa 2/genética , Antineoplásicos/farmacología , Antineoplásicos/química , Phaeophyceae/química , Mitocondrias/metabolismo , Mitocondrias/efectos de los fármacos , Caspasa 3/metabolismo , Caspasa 3/genética

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The aerobic composting process is extensively utilized to manage kitchen waste. Nonetheless, the variability in the quality of compost derived from engineering practices which significantly hinders its broader industrial application. This work investigated the final products of kitchen waste compost at multiple industrial-scale treatment facilities utilizing three distinct aerobic composting processes in a bid to explore key factors affecting compost quality. The quality evaluation was based on technical parameters like seed germination index (GI), and limiting factors such as heavy metal content. The results showed that most of the compost products failed to meet the established standards, with GI being the primary limiting indicator. Furthermore, maturity assessments suggested that compost with low GI exhibited reduced humification could not be recommended for agricultural use. The investigation delved into the primary determinants of GI, focusing on risk factors such as the oil and salt of kitchen waste, and the microbial community of the humification driving forces. The results indicated that products with low GI had higher oil and salt content and a relatively simple microbial community. A thorough analysis suggested that excessive levels oil and salt were potential influencing factors on GI, as they stimulated the activity of acid-producing bacteria like Lactobacillus, suppressed the activity of humification-promoting bacteria such as Actinomarinales, and influenced the decomposition and humification processes of organic matter and total nitrogen, thereby affecting product quality. The findings provide valuable insights for improving kitchen waste compost products for agricultural applications.

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Ossification of the ligamentum flavum (OLF) is the primary etiology of thoracic spinal stenosis. The functional properties of epidural fat (EF), an adipose tissue located in close proximity to ligamentum flavum (LF), have been scarcely investigated. The metabolic state of adipocytes significantly influences their functionality, and exosomes play a pivotal role in intercellular communication. This study aimed to investigate the role of EF-derived exosomes in OLF and characterize their protein profile by proteomics analysis. Our findings demonstrate that exosomes obtained from EF adjacent to OLF possess the ability to enhance osteogenesis of fibroblasts in vitro. Furthermore, proteomics analysis revealed metabolic dysfunction in EF adipocytes and identified lactate dehydrogenase A (LDHA) as a potential mediator involved in the development of OLF. This study provides new insights into the pathogenic mechanism underlying OLF and offers a theoretical basis for preventing and treating ligament ossification.

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The poor interfacial compatibility of natural fiber-reinforced polymer composites has become a major challenge in the development of industry-standard high-performance composites. To solve this problem, this study constructs a novel rigid-flexible balanced molecular crosslinked network transition interface in composites. The interface improves the interfacial compatibility of the composites by balancing the stiffness and strength of the fibers and the matrix， effectively improving the properties of the composites. The flexural strength and flexural modulus of the composites were enhanced by 38 % and 44 %, respectively. Water absorption decreased by 30 %. The initial and maximum thermal degradation temperatures increased by 20 °C and 16 °C, respectively. The maximum storage modulus increased by 316 %. Furthermore, the impact toughness was elevated by 41 %, attributed to the crosslinked network's efficacy in absorbing and dissipating externally applied energy. This innovative approach introduces a new theory of interfacial reinforcement compatibility, advancing the development of high-performance and sustainable biocomposites.

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Materiales Biocompatibles , Materiales Biocompatibles/química , Butadienos/química , Ensayo de Materiales , Reactivos de Enlaces Cruzados/química , Sasa/química , Polímeros/química , Temperatura , Agua/química , Adipatos/química , Resistencia a la Tracción

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Background: Despite this growing interest, there remains a lack of comprehensive and systematic bibliometric analyses of ketamine research. This study aimed to summarize the progress in ketamine research through bibliometric analysis, providing insights into the development and direction of the field. Methods: Publications related to ketamine were retrieved from the Web of Science Core Collection (WoSCC) database on February 15, 2024. In conducting a comprehensive bibliometric analysis, a variety of bibliographic elements were meticulously collected to map the landscape of research within a specific field. Results: Between January 1, 2014, and December 31, 2023, a total of 10,328 articles on ketamine research were published across 1,752 academic journals by 45,891 authors from 8,914 institutions in 128 countries. The publication volume has shown a steady increase over this period. The United States of America (USA) and the People's Republic of China lead in both publication and citation counts. The National Institute of Mental Health (NIMH) and Yale University emerge as the most active institutions in this research domain. Carlos Zarate of the NIH National Institute of Mental Health was noted for the highest number of significant publications and received the most co-citations. The analysis revealed key research themes including mechanism of action, adverse events, psychiatric applications, and perioperative implications. Conclusion: This study provided comprehensive bibliometric and knowledge mapping analysis of the global ketamine research landscape, offering valuable insights into the trends, key contributors, and thematic focus areas within the field. By delineating the evolution of ketamine research, this study aims to guide future scholarly endeavors and enhance our understanding of ketamine's therapeutic potential.

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Hepatocellular carcinoma (HCC), a prevalent malignancy worldwide, poses significant challenges in terms of prognosis, necessitating innovative therapeutic approaches. Ferroptosis offers notable advantages over apoptosis, holding promise as a novel therapeutic approach for HCC complexities. Moreover, while the interaction between long non-coding RNAs (lncRNAs) and mRNAs is pivotal in various physiological and pathological processes, their involvement in ferroptosis remains relatively unexplored. In this study, we constructed a ferroptosis-related lncRNA-mRNA correlation network in HCC using Pearson correlation analysis. Notably, the SLC7A11-AS1/SLC7A11 pair, exhibiting high correlation, was identified. Bioinformatics analysis revealed a significant correlation between the expression levels of this pair and key clinical characteristics of HCC patients, including gender, pathology, Ishak scores and tumour size. And poor prognosis was associated with high expression of this pair. Functional experiments demonstrated that SLC7A11-AS1, by binding to the 3'UTR region of SLC7A11 mRNA, enhanced its stability, thereby promoting HCC cell growth and resistance to erastin- induced ferroptosis. Additionally, in vivo studies confirmed that SLC7A11-AS1 knockdown potentiated the inhibitory effects of erastin on tumour growth. Overall, our findings suggest that targeting the SLC7A11-AS1/SLC7A11 pair holds promise as a potential therapeutic strategy for HCC patients.

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Sistema de Transporte de Aminoácidos y+ , Carcinoma Hepatocelular , Ferroptosis , Regulación Neoplásica de la Expresión Génica , Neoplasias Hepáticas , ARN Largo no Codificante , Ferroptosis/genética , Humanos , Carcinoma Hepatocelular/genética , Carcinoma Hepatocelular/patología , Carcinoma Hepatocelular/metabolismo , Carcinoma Hepatocelular/tratamiento farmacológico , Neoplasias Hepáticas/genética , Neoplasias Hepáticas/patología , Neoplasias Hepáticas/metabolismo , Neoplasias Hepáticas/tratamiento farmacológico , Sistema de Transporte de Aminoácidos y+/genética , Sistema de Transporte de Aminoácidos y+/metabolismo , ARN Largo no Codificante/genética , ARN Largo no Codificante/metabolismo , Animales , Línea Celular Tumoral , Masculino , Femenino , Ratones , Pronóstico , Proliferación Celular/genética , Ratones Desnudos , Persona de Mediana Edad , ARN Mensajero/genética , ARN Mensajero/metabolismo , Piperazinas/farmacología

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OBJECTIVES: To identify lymphatic vascular space invasion (LVSI) and lymphatic node metastasis (LNM) status of endometrial cancer (EC) patients, using radiomics based on MRI images. METHODS: Five hundred and ninety-eight EC patients between January 2015 and September 2020 from two institutions were retrospectively included. Tumoral regions on DWI, T1CE, and T2W images were manually outlined. Radiomics features were extracted from tumor region and peri-tumor region of different thicknesses. We established sub-models to select features from each smaller category. Using this method, we separately constructed radiomic signatures for intra-tumoral and peri-tumoral images using different sequences. We constructed intra-tumoral and peri-tumoral models by combining their features, and a multi-sequence model by combining logits. Models were trained with 397 patients and validated with 170 internal and 31 external patients. RESULTS: For LVSI positive/LNM positive status identification, the multi-parameter MRI radiomics model achieved the area under curve (AUC) values of 0.771 (95%CI: [0.692-0.849])/0.801 (95%CI: [0.704, 0.898]) and 0.864 (95%CI: [0.728-1.000])/0.976 (95%CI: [0.919, 1.000]) in internal and external test cohorts, respectively. CONCLUSIONS: Intra-tumoral and peri-tumoral radiomics signatures based on mpMRI can both be used to identify LVSI or LNM status in EC patients non-invasively. Further studies on LVSI and LNM should pay attention to both of them.

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Ferroptosis is an iron-dependent programmed cell death (PCD) enforced by lipid peroxidation accumulation. Transferrin receptor (TFRC), one of the signature proteins of ferroptosis, is abundantly expressed in hepatocellular carcinoma (HCC). However, post-translational modification (PTM) of TFRC and the underlying mechanisms for ferroptosis regulation remain less understood. In this study, we found that TFRC undergoes O-GlcNAcylation, influencing Erastin-induced ferroptosis sensitivity in hepatocytes. Further mechanistic studies found that Erastin can trigger de-O-GlcNAcylation of TFRC at serine 687 (Ser687), which diminishes the binding of ubiquitin E3 ligase membrane-associated RING-CH8 (MARCH8) and decreases polyubiquitination on lysine 665 (Lys665), thereby enhancing TFRC stability that favors labile iron accumulation. Therefore, our findings report O-GlcNAcylation on an important regulatory protein of ferroptosis and reveal an intriguing mechanism by which HCC ferroptosis is controlled by an iron metabolism pathway.

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Carcinoma Hepatocelular , Ferroptosis , Neoplasias Hepáticas , Receptores de Transferrina , Receptores de Transferrina/metabolismo , Receptores de Transferrina/genética , Humanos , Carcinoma Hepatocelular/metabolismo , Carcinoma Hepatocelular/patología , Carcinoma Hepatocelular/genética , Neoplasias Hepáticas/metabolismo , Neoplasias Hepáticas/patología , Neoplasias Hepáticas/genética , Procesamiento Proteico-Postraduccional , Línea Celular Tumoral , Hierro/metabolismo , Ubiquitinación , Glicosilación , Estabilidad Proteica , Piperazinas

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OBJECTIVES: We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MRI. METHODS: We retrospectively recruited 227 patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the Cancer Genome Atlas. Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the nonendometrioid, high-risk, and P53abn EC group. RESULTS: The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from nonendometrioid cancer and high-risk from low- and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all 3 tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). CONCLUSION: In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating nonendometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC. ADVANCES IN KNOWLEDGE: (1) The contrast-enhanced T1WI was the best MRI sequence for differentiation P53abn from the non-P53abn group (test AUC: 0.8). (2) The radiomics-based diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC: 0.834 vs 0.682). (3) The proposed model derived from multi-parametric MRI images achieved a higher accuracy in P53abn EC identification (AUC: 0.845).

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Neoplasias Endometriales , Imagen por Resonancia Magnética , Nomogramas , Proteína p53 Supresora de Tumor , Humanos , Femenino , Neoplasias Endometriales/diagnóstico por imagen , Estudios Retrospectivos , Persona de Mediana Edad , Imagen por Resonancia Magnética/métodos , Proteína p53 Supresora de Tumor/genética , Anciano , Mutación , Adulto

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PURPOSE: Traditionally, the epidural fat (EF) is known as a physical buffer for the dural sac against the force and a lubricant facilitating the relative motion of the latter on the osseous spine. Along with the development of the studies on EF, controversies still exist on vital questions, such as the underlying mechanism of the spinal epidural lipomatosis. Meanwhile, the scattered and fragmented researches hinder the global insight into the seemingly dispensable tissue. METHODS: Herein, we reviewed literature on the EF and its derivatives to elucidate the dynamic change and complex function of EF in the local milieu, especially at the pathophysiological conditions. We start with an introduction to EF and the current pathogenic landscape, emphasizing the interlink between the EF and adjacent structures. We generally categorize the major pathological changes of the EF into hypertrophy, atrophy, and inflammation. RESULTS AND CONCLUSIONS: It is acknowledged that not only the EF (or its cellular components) may be influenced by various endogenic/exogenic and focal/systematic stimuli, but the adjacent structures can also in turn be affected by the EF, which may be a hidden pathogenic clue for specific spinal disease. Meanwhile, the unrevealed sections, which are also the directions the future research, are proposed according to the objective result and rational inference. Further effort should be taken to reveal the underlying mechanism and develop novel therapeutic pathways for the relevant diseases.

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Espacio Epidural , Lipomatosis , Humanos , Espacio Epidural/patología , Imagen por Resonancia Magnética/métodos , Lipomatosis/patología , Huesos/patología

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Objective: This study aims to analyze the drug resistance spectrum, genetic diversity, and transmission dynamics to provide a basis for the prevention and control of drug-resistant (DR) tuberculosis (TB) epidemics. Methods: This retrospective study is based on routine national drug resistance surveillance. The demographic, epidemiological, and clinical information on DR-TB patients from 2016 to 2021 was collected and used for phenotypic drug susceptibility testing and whole-genome sequencing. Results: It was indicated that L2.2.1 was the dominant lineage in Urumqi. The drug resistance spectrum in Urumqi was narrow, which means more drug combinations can be used for clinical treatment. Furthermore, mutations identification of drug-resistance gene katG, rpoB, embA/B, rrs, rpsL, eis, gyrA/B, folC and tryA are important for clinical drug use. However, mutations in cross-resistance genes rrs have limited guidance for clinical selection of KM, CPM and AK. Moreover, there is an increased risk of cluster transmission of DR-TB, and the difference in clustering rate among L2, L3, and L4 was not statistically significant (χ2 = 2.6410, p = 0.2670). Conclusion: In the Urumqi, DR-TB has a complex prevalence state, a narrow drug resistance spectrum, and a high clustering rate and burden of drug resistance. To reduce the burden of DR-TB, related research should be strengthened, and the development of prevention, control, and treatment strategies should be accelerated.

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BACKGROUND: Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi-parametric MRI (mpMRI) images. PURPOSE: To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images. STUDY TYPE: Retrospective. POPULATION: Six hundred twenty-one patients with histologically proven EC from two institutions, including 111 LNM-positive and 168 LVSI-positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively. FIELD STRENGTH/SEQUENCE: T2-weighted imaging (T2WI), contrast-enhanced T1WI (CE-T1WI), and diffusion-weighted imaging (DWI) were scanned with turbo spin-echo, gradient-echo, and two-dimensional echo-planar sequences, using either a 1.5 T or 3 T system. ASSESSMENT: EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU-Net model for automatic segmentation. A multi-task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE-T1WI, and DWI images as inputs. The performance of the model for LNM-positive diagnosis was compared with those of three radiologists in the external test cohort. STATISTICAL TESTS: Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant. RESULTS: EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674). DATA CONCLUSION: The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

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OBJECTIVE: We aimed to differentiate granulosa cell tumors (GCT) from other ovarian sex-cord tumors (OSCs) based on feature analysis of the tumor body on MR imaging. METHODS: We retrospectively enrolled 27 patients with pathologically proven sex-cord tumours (14 GSTs, 8 fibromas, 4 fibrothecomas, and 1 sclerosing stromal tumour) from our institution. All MRI examinations were performed at least one month prior to surgery. MR image features were recorded by two radiologists with consensus readings. Histogram analysis was performed using FeAture Explorer software. The differences in histogram parameters between GCT (38.1 ± 14.6 years) and OSC (43.7 ± 18.0 years) groups were compared. Fourteen randomly selected cellular-type myomas who also underwent MRI in our hospital were considered as the control group. The intra-operator consistency of ADC value was evaluated across measurements twice. RESULTS: The repeatability of conventional ADC measurements on the tumor body was good. The values of ADC-mean, ADC-min, and ADC-max significantly differed across three groups (p < 0.001). The histogram variance on DWI, histogram percentage on T2WI, and ADC min showed the best discriminative performance in determining GCTs from other OSCs with an area under the receiver operator curve (AUC) of 0.997, 0.882, and 0.795, respectively. The histogram variance on DWI yielded a sensitivity of 92.3%, a specificity of 100%, and an accuracy of 96.6% in discriminating GSTs from other OSCs. CONCLUSION: In the present study, feature analysis of tumor body MR imaging has helped to differentiate GST from OSC with better performance than conventional ADC measurements.

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BACKGROUND: Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. PURPOSE: To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. STUDY TYPE: Prospective. POPULATION/SUBJECTS: 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. FIELD STRENGTH/SEQUENCE: 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. ASSESSMENT: A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. STATISTICAL TESTS: We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. RESULTS: NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. DATA CONCLUSION: Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

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BACKGROUND: The diagnosis of prenatal placenta accreta spectrum (PAS) with magnetic resonance imaging (MRI) is highly dependent on radiologists' experience. A deep learning (DL) method using the prior knowledge that PAS-related signs are generally found along the utero-placental borderline (UPB) may help radiologists, especially those with less experience, to mitigate this issue. PURPOSE: To develop a DL tool for antenatal diagnosis of PAS using T2-weighted MR images. STUDY TYPE: Retrospective. SUBJECTS: Five hundred and forty pregnant women with clinically suspected PAS disorders from two institutions, divided into training (409), internal test (103), and external test (28) datasets. FIELD STRENGTH/SEQUENCE: Sagittal T2-weighted fast spin echo sequence at 1.5 T and 3 T. ASSESSMENT: An nnU-Net was trained for placenta segmentation. The UPB straightening approach was used to extract the utero-placental boundary region. The UPB image was then fed into DenseNet-PAS for PAS diagnosis. DenseNet-PP learnt placental position information to improve the PAS diagnosis performance. Three radiologists with 8, 10, and 12 years of experience independently evaluated the images. Two radiologists marked the placenta tissue. Histopathological findings were the reference standard. STATISTICAL TESTS: Area under the curve (AUC) was used to evaluate the classification. Dice coefficient evaluated the segmentation between radiologists and the model performance. The Mann-Whitney U-test or the chi-squared test assessed the significance of differences. Decision curve analysis was used to determine clinical effectiveness. DeLong's test was used to compare AUCs. RESULTS: Of the 540 patients, 170 had PAS disorders confirmed by histopathology. The DL model using UPB images and placental position yielded the highest AUC of 0.860 and 0.897 in internal test and external test cohorts, respectively, significantly exceeding the performance of three radiologists (internal test AUC, 0.737-0.770). DATA CONCLUSION: By extracting the UPB image, this fully automatic DL pipeline achieved high accuracy and may assist radiologists in PAS diagnosis using MRI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

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Aprendizaje Profundo , Placenta Accreta , Femenino , Embarazo , Humanos , Placenta , Placenta Accreta/diagnóstico por imagen , Estudios Retrospectivos , Imagen por Resonancia Magnética/métodos

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Introduction: The role of quantitative target setting has become an important topic in debates on the improvement of road safety performance. Specifically, there are questions regarding the relationship between quantitative safety targets and their actual effects. Although previous studies have provided important insights into this subject, their empirical findings have largely been equivocal, and research on this topic remains inadequate. Methods: Based on panel data representing 20 years of observations from 34 OECD member states, we employed nonlinear and linear panel models to investigate whether and how the attributes of quantitative road safety targets (i.e., target ambition and duration) influence their success (i.e., target completion status and rate). Results: The results indicate that a quantitative target with a higher level of ambition is associated with a lower likelihood and rate of completion, whereas there is no support for a connection between target duration and final completion rate. This suggests that an excessively ambitious target does not necessarily result in better road safety performance and is detrimental to achieving expected fatality reductions. Conclusion: From an empirical perspective, this study revealed a potential interaction effect between quantitative road safety targets and practical fatality reduction performance, providing government officials and policymakers with essential references for future practices on target setting and governance planning in regard to public health.

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Accidentes de Tránsito , Accidentes de Tránsito/prevención & control , Seguridad , Probabilidad

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BACKGROUND: Breast cancer is the most common cancer worldwide, with the fifth highest mortality rate among all cancers and high risk of metastasis. However, potential biomarkers and molecular mechanisms underlying the stratification of breast cancer in terms of clinical outcomes remain to be investigated. Therefore, we aimed to find a novel prognostic biomarker and therapeutic target for breast cancer patients. METHODS: Unsupervised hierarchical clustering was used to perform comprehensive transcriptomic study of total 185 glycogenes in public datasets of breast cancer with clinicopathological and survival information. A glycogene-based signature for subtype classification was discovered using Limma packages, and relevance to four known molecular features was identified by GSVA. Experimental verification was performed and biological functions of B3GNT7 were characterized by quantitative RT-PCR, western blot, transwell assays, and lectin immunofluorescence staining in breast cancer cells. RESULTS: A 23-glycogene signature was identified for the classification of breast cancer. Among the 23 glycogenes, B3GNTs showed significantly positive associations with ER-/Her2- subtype in breast cancer patients (n = 2655). Overexpressed B3GNT7 were correlated with poor prognosis in breast cancer patients based on public datasets. B3GNT7 depletion inhibited cell proliferation, migration, and invasion, and decreased global fucosylation in MDA-MB-231 and HCC1937 breast cancer cells. CONCLUSIONS: Herein, we discovered a unique 23-gene signature for breast cancer patient glycogene-type classification. Among these genes, B3GNT7 was shown to be a potential biomarker for unfavorable outcomes and therapeutic target of breast cancer.

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Neoplasias de la Mama , Femenino , Humanos , Neoplasias de la Mama/genética , Neoplasias de la Mama/patología , Perfilación de la Expresión Génica , N-Acetilglucosaminiltransferasas/genética , N-Acetilglucosaminiltransferasas/metabolismo , Pronóstico , Transcriptoma , Biomarcadores de Tumor/genética

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The same visual input can serve as the target of perception or as a trigger for memory retrieval depending on whether cognitive processing is externally oriented (perception) or internally oriented (memory retrieval). While numerous human neuroimaging studies have characterized how visual stimuli are differentially processed during perception versus memory retrieval, perception and memory retrieval may also be associated with distinct neural states that are independent of stimulus-evoked neural activity. Here, we combined human fMRI with full correlation matrix analysis (FCMA) to reveal potential differences in "background" functional connectivity across perception and memory retrieval states. We found that perception and retrieval states could be discriminated with high accuracy based on patterns of connectivity across (1) the control network, (2) the default mode network (DMN), and (3) retrosplenial cortex (RSC). In particular, clusters in the control network increased connectivity with each other during the perception state, whereas clusters in the DMN were more strongly coupled during the retrieval state. Interestingly, RSC switched its coupling between networks as the cognitive state shifted from retrieval to perception. Finally, we show that background connectivity (1) was fully independent from stimulus-related variance in the signal and, further, (2) captured distinct aspects of cognitive states compared to traditional classification of stimulus-evoked responses. Together, our results reveal that perception and memory retrieval are associated with sustained cognitive states that manifest as distinct patterns of connectivity among large-scale brain networks.

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Memoria Episódica , Memoria , Humanos , Memoria/fisiología , Encéfalo/fisiología , Imagen por Resonancia Magnética/métodos , Neuroimagen , Percepción , Mapeo Encefálico , Vías Nerviosas/fisiología

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Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution in deep gray matter (DGM) nuclei. We hypothesized that deep learning (DL) could be used to automatically segment all DGM nuclei and use relevant features for a better differentiation between PD and healthy controls (HC). In this study, we proposed a DL-based pipeline for automatic PD diagnosis based on QSM and T1-weighted (T1W) images. This consists of (1) a convolutional neural network model integrated with multiple attention mechanisms which simultaneously segments caudate nucleus, globus pallidus, putamen, red nucleus, and substantia nigra from QSM and T1W images, and (2) an SE-ResNeXt50 model with an anatomical attention mechanism, which uses QSM data and the segmented nuclei to distinguish PD from HC. The mean dice values for segmentation of the five DGM nuclei are all >0.83 in the internal testing cohort, suggesting that the model could segment brain nuclei accurately. The proposed PD diagnosis model achieved area under the the receiver operating characteristic curve (AUCs) of 0.901 and 0.845 on independent internal and external testing cohorts, respectively. Gradient-weighted class activation mapping (Grad-CAM) heatmaps were used to identify contributing nuclei for PD diagnosis on patient level. In conclusion, the proposed approach can potentially be used as an automatic, explainable pipeline for PD diagnosis in a clinical setting.

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Aprendizaje Profundo , Enfermedad de Parkinson , Humanos , Enfermedad de Parkinson/diagnóstico por imagen , Sustancia Gris/diagnóstico por imagen , Globo Pálido , Núcleo Caudado , Imagen por Resonancia Magnética/métodos , Mapeo Encefálico/métodos

RESUMEN

Solid carbon source (SCS) has attracted increasing research interests considering its merits of sustainable organic matter release capacity, safe transportation, easy management, and no need for frequent addition. In this study, the organic matter release capacities of five selected natural (milled rice and brown rice) and synthetic (PLA, PHA, PCL) SCSs were systematically investigated. The results showed that brown rice was the preferable SCS with high COD release potential, release rate and maximum accumulation of 309.2 mg-COD/g-SCS, 581.3 mg-COD/L·d and 6183.3 mg-COD/L, respectively. The cost for COD supply of brown rice stood at $1.0/kg-COD with considerable economic viability. The organic matter release process of brown rice could be well depicted by Hixson-Crowell model with a rate constant of -1.10. The addition of activated sludge could enhance the organic matter release of brown rice, evidenced by the increased release of VFAs with a proportion up to 97.1 % in the total organic matter. Moreover, the mass flow of carbon showed that the addition of activated sludge could improve the carbon utilization rate, and the peak value could achieve 45.4 % in 12 days. The unique dual-enzyme system, consisting of exogenous hydrolase from microorganisms in activated sludge and the endogenous amylase from brown rice, was supposed to be the main reason for the superior carbon release capacity of brown rice over other SCSs. This study was expected to offer an economic and effective SCS for the biological treatment of low-carbon wastewater.

Asunto(s)

Oryza , Purificación del Agua , Aguas del Alcantarillado , Eliminación de Residuos Líquidos/métodos , Carbono , Reactores Biológicos